Image processing

ABSTRACT

Embodiments of the present invention relate to image processing an enhancement relative to the color emotion domain. In one embodiment, an image processor comprises an input ( 202 ) to receive image pixel data and an input ( 204 ) to receive color emotion indicia, the processor being arranged to adjust target pixels of the image in a color encoding space on the basis of the color emotion indicia and to output image pixel data resulting therefrom.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a National Stage application of InternationalApplication No. PCT/US2009/031427, filed Jan. 20, 2009, the disclosureof which are hereby incorporated by reference in its entirety.

FIELD

The present invention relates to image processing and, moreparticularly, to image enhancement relative to the colour emotiondomain.

BACKGROUND

It is known to vary the technical characteristics of a digital image,for example a digital photograph, by removing technical faultsintroduced by a camera that took the photograph, or by modifying thetechnical characteristics of the image to make it more “pleasing”. Forexample, it is known to adjust sharpness and contrast, reduce noise,correct red-eye and even modify certain colours, such as colours of theskin, grass or sky. However, along with the technical qualities, imagespossess emotional qualities: for example, images can appear “sad” or“happy”, “upsetting” or “relaxing”, “warm” or “chilly”, and so on.Enhancing an image's technical characteristics can result in a lesssatisfactory image, in which the emotion has been diluted or lost. Forexample, colours of an image photographed in candle light can beadjusted to be perfectly balanced from a technical point of view but, bydoing so, the “warmness” of the image can be destroyed. On the otherhand, improving the emotional characteristics of such an image byenhancing its “warmth” can result in a better image even if this is doneat the expense of degrading its “technical” qualities.

SUMMARY

According to a first aspect, the present invention provides an imageprocessor comprising an input to receive image data and an input toreceive colour emotion indicia, the processor being configured to adjustimage attributes in a colour encoding space on the basis of the colouremotion indicia and to output an image resulting therefrom.

BRIEF DESCRIPTION OF THE DRAWINGS

Various features and advantages of the invention will become apparentfrom the following description of embodiments of the invention, given byway of example only, which is made with reference to the accompanyingdrawings, of which:

FIG. 1 is a diagram of a prior art imaging system in which embodimentsof the present invention find application;

FIG. 2 is a diagram of an image processor according to a firstembodiment of the present invention;

FIG. 3 is a diagram of an image processor according to a secondembodiment of the present invention;

FIG. 4 is a graph showing a two-dimensional colour emotion model;

FIG. 5 is a diagram of an image processor according to a thirdembodiment of the present invention; and

FIG. 6 is a flow diagram illustrating an image enhancement processaccording to an embodiment of the present invention.

DETAILED DESCRIPTION

Various embodiments of the present invention will now be described inmore detail with reference to the accompanying drawings. It will beappreciated that the invention is not limited in its application to thedetails of method and the arrangement of components as set forth in thefollowing description or illustrated in the drawings. It will beapparent to a person skilled in the art that additional embodiments ofthe present invention not detailed in the description are possible andwill fall within the scope of the present claims. Accordingly, thefollowing description should not be interpreted as limiting in any way,and the scope of protection is defined solely by the claims appendedhereto.

The diagram in FIG. 1 is a schematic diagram of a basic imaging systemof the kind known from the prior art. The same general system can beused according to embodiments of the present invention, though withdifferent image enhancement processes being used, as will be described.According to FIG. 1, digital images are captured using a camera 100 andare downloaded onto a personal computer system 105. Selected images arethen sent via a network 110, for example the Internet, to a computer 115in a photographic printing laboratory. The computer 115 stores theimages in a data store 120 in a print queue. The images are thenretrieved by an imaging computer 125, which processes the images forexample as described above with reference to FIGS. 7 a and 7 b, andsends them to a printer 130 to be printed. The printed images aresubsequently checked for quality—typically a manual process—and sent tothe originator. The computers in this exemplary system all typicallyoperate under a Microsoft Windows™ operating system; but could equallybe operating under another operating system such as Linux™.

While the image processing is shown as being carried out on an imagingcomputer 125, it will be appreciated that some or all of the imageprocessing could be carried out at other points in the system. Forexample, at least a part of the image processing could occur on thepersonal computer system 105, on the photographic laboratory computer115 or even in the printer 130 itself, if provided with appropriateprocessing capability. Such processing could occur at any otherappropriate point in this or any other appropriate system. For example,the image processing could even take place on the camera itself.

Embodiments of the present invention relate to image processingpipelines that enhance images in relation to desired perceptualqualities such as ‘emotion’ and ‘affect’. Such qualities are thought ofas ‘artistic’ qualities rather than technical characteristics of animage. En effect, embodiments of the present invention determine adesired image enhancement in a colour emotion domain and map theenhancement from the colour emotion domain to a colour encoding domain,as will be described below.

While embodiments of the present invention are described in relation toimage enhancement for printed imams, they apply to image enhancement ingeneral and, for example, could be applied (without limitation) toelectronically displayed images, to be displayed on any kind ofelectronic display or projected onto a screen.

Before describing embodiments of the present invention, some backgroundwill now be provided. The perceptual quality of ‘colour emotion’ is anactive area of colour science research. It aims at developingpsychophysical measures to be used in automated decision tools forgeneration of colour palettes having predefined emotional properties. Incolour emotion studies, observers view a colour stimulus: a colourpatch, or a combination of patches. Observers then grade their emotionalresponse to the stimulus on some scale with psychologically-definedends: for example “fresh-stale”, “hard-soft”, “tense-relaxed”. Ou et al[1] conducted series or such experiments in which observers scored eachcolour against 10 such colour-emotion pairs (each pair defining arespective colour-emotion scale): warm-cool, heavy-light,modern-classical, clean-dirty, active-passive, hard-soft, tense-relaxed,fresh-stale, masculine-feminine and like-dislike. Ou et al alsoconcluded that all 10 colour emotion pairs can be represented in only athree-dimensional colour emotion space (within the colour emotiondomain) having the orthogonal axes (representing three colour-emotionscales defining the space) “colour activity” (active-passive scale),“colour weight” (heavy-light scale) and “colour warmth (warm-coolscale). Of the scales: colour activity comprises the three scalesactive-passive, clean-dirty and modern-classical: colour weightcomprises the scales hard-soft. masculine-feminine and heavy-light; andcolour warmth comprises the warm-cool scale. In practical terms, Ou etal have developed a forward mapping from the three-dimensional CIELABcolour encoding space (based on the three axes of lightness, green-redand blue-yellow) to a colour emotion space (based on the three colouremotion scales) within the colour emotion domain, which can be used tochoose colour palettes having predefined emotional or harmonicproperties.

It will be appreciated that many other colour emotion spaces, other thanthe one conceived by [1], exist within the colour emotion domain, andembodiments of the present invention are in no way limited to any oneparticular colour space.

In arriving at embodiments of the present invention, the presentinventors have developed inverse colour emotion correction models thatcan be used to modify the emotional quality of full colour images. Inparticular, the present inventors have devised an approach to alteringimage attributes to change the emotional quality of an image, involvingediting an image in a device-independent colour space, for exampleCIELAB or CIACAM02, while using the colour emotion model in [1] to guidethe direction and magnitude. In particular, the present inventors havefound that it is possible to use the colour emotion scales in [1] tomodify images, whereby each pixel can be represented by coordinates inthree-dimensional colour-emotion space, and the colour emotion value ofthe image is the mean of its pixels (though, in principle, according toembodiments of the invention, the colour emotion value could be based ona different statistical measure, such as median, mode, or the like). Inaddition, the present inventors have applied Sato's [2] principlewherein, in a uniform colour encoding space, a colour emotion value wassaid to be related to the distance (Euclidian, angular or other) fromthe colour having the minimum colour emotion grade on a relevant scaleor, alternatively, inversely-related to the distance from the colourhaving the maximum colour emotion grade on a relevant scale This valuewill be referred to hereinafter as the ‘colour emotion reference’ andcan be defined in terms of a single point in the colour encoding space(such as CIELAB L*, a* and b*) or a set of points having some commonproperty, for example having the same CIELAB hue angle value h*.

Three exemplary inverse colour emotion correction models will bedescribed below. In particular, according to the embodiments describedherein, the models relate to colour activity, colour weight and colourwarmth, which were selected based on the teaching in [1] to be the threeaxes in a three-dimensional colour emotion space within which imageadjustments are made. The models are described in relation to CIELAB.However, it is emphasised that any other encoding colour space can beused, including device-independent spaces such as CIECAM02 anddevice-dependent spaces such as RGB or CMYK, provided the principlesdescribed herein are followed. It is also noted that the CIELAB valuesare computed according to standard formulae published by the CIE (CIE.1978. Recommendations on uniform color spaces, color-differenceequations, psychometric color terms. Supplement No. 2 of the CIEPublication No 15 (E-1.3.1) 1971. CIE, Central Bureau of the CIE, Paris,France).

Colour Activity

According to the present embodiments, the colour emotion reference isset to be a muddy greyish yellow corresponding to CIELAB coordinates[50, 3, 17] according to the finding in [1]. Hence, the measured colouractivity of each image pixel is modified by Equation (1), for L*_(a),a*_(a) and b*_(a):

$\begin{matrix}{{{L_{a}^{*} = {{L_{0}^{*}\left( {1 + k_{a}} \right)} + 50}};}{{a_{a}^{*} = {{a_{0}^{*}\left( {1 + k_{a}} \right)} + 3}};}{b_{a}^{*} = {{b_{0}^{*}\left( {1 + \frac{k_{a}}{1.4}} \right)} + 17}}} & {{Equation}\mspace{14mu}(1)}\end{matrix}$

The three CIELAB coordinates represent the lightness of the colour (L*=0yields black and L*=100 indicates white), its position betweenred/magenta and green (a*, negative values indicate green while positivevalues indicate magenta) and its position between yellow and blue (b*,negative values indicate blue and positive values indicate yellow). InEquation (1), k_(a) is a correction coefficient controlling the extentof “colour activation” or “colour deactivation”, whereby positive k_(a)values lead to increased activity and negative values lead to reducedactivity. L*₀, a*₀ and b*₀ are the pixel's CIELAB coordinates shifted tothe activity colour emotion reference; that is L*₀ L*-50; a*₀ a*-3; b*₀b*-17. In practice, an image processor according to embodiments of thepresent invention modifies the colour activity of each target pixel onthe basis of a value of k_(a) supplied to the processor.

The perceptual equivalent of Equation (1) is the modification of thelightness contrast and chroma contrast. However, the chroma contrast ismodified relative to the activity colour emotion reference (that is,modifying the distance from the point [a=−3, b=17]), rather than theCIELAB origin. In doing so, the hue also changes by a small amount. Asthe result, the positive “activity” enhancement reduces the low-chromayellowish tones while simultaneously increasing the chroma of the restof the colours, which gives the feeling of a “cleaner” and more vividimage. Hence, more active images (relative to the original) will havehigher lightness contrast with tones shifted to higher chroma blues andyellows; less active ones will be lower in lightness contrast and inchroma, and will have more yellowish low-chroma tones.

Colour Weight

In the model in [1], colour weight is determined by a combination ofEuclidian distance of the L* coordinate from the white L*=100, and ofthe angular distance of colour's hue from the weight colour emotionreference hue h*-100, as shown in Equation (2):

$\begin{matrix}{h_{w} = \left\{ {{\begin{matrix}{{h_{0} + {h_{0}\frac{\left( {180 - h_{0}} \right)}{180}w_{C}k_{w}} + 100};} & {h_{0} \leq 180} \\{{h_{0} + {\left( {360 - h_{0}} \right)\frac{\left( {h_{0} - 180} \right)}{180}w_{C}k_{w}} + 100};} & {h_{0} > 180}\end{matrix}L_{w}} = {100\left( \frac{L}{100} \right)^{7}}} \right.} & {{Equation}\mspace{14mu}(2)}\end{matrix}$

Here, k_(w) is a correction coefficient controlling the extent ofincreasing or reducing the colour weight for a target pixel, wherebypositive k_(w) values lead to increased weight and negative values leadto reduced weight: h₀ is the pixel's hue shifted towards the weightcolour emotion reference hue, i.e. h₀−h−100, and w_(C) is a weightingfactor set according to the chroma C* of the original pixel as inEquation (3):

$\begin{matrix}{w_{C} = \left( {1 - \frac{C^{*}}{150}} \right)^{3}} & {{Equation}\mspace{14mu}(3)}\end{matrix}$

Other chroma weighting functions may be applied by the skilled person.

As shown, the lightness L of the pixel is modified by a gamma function,whereby γ is the function of the correction magnitude and direction,that is γ−f(k). so that γ>1 to increase colour weight, and 0<γ<1 toreduce it.

The perceptual equivalent of colour weight can be described as theoverall degree of lightness and “yellowness”. Lighter (than theoriginal) images will be yellower and brighter, with no deep shadows andhigh-contrast areas. Heavier images, on the other hand, will be bluer,dominated by shadows.

Colour Warmth

In the model in [1] the colour warmth index is dependent on a pixelcolour's chroma and the angular distance from a warmth colour emotionreference hue, h 50. According to the present embodiment, chroma ismodified according to Equation (4):C _(H) *=C* ^((1-0.03k))  Equation (4)and hue is modified as described in Equation (2), but using a differentwarmth colour emotion reference hue value of h₀−h−50, and replacing thecoefficient k_(w) with a different correction coefficient k_(H)controlling the extent of “colour warming” or “colour cooling” for atarget pixel, whereby positive k_(H) values lead to increased warmth andnegative values lead to reduced warmth.

The perceptual effect of enhancing the colour warmth is mostly in makingthe image more “red” and slightly more vivid (having higher chroma):where red and chroma are reduced to reduce the warmth.

Three alternative embodiments of the present invention will now bedescribed by way of example. Each embodiment is an image processor whichforms an image processing pipeline or elements thereof. In each case,the pipeline may comprise other elements (that are not shown forsimplicity of understanding only), which may be upstream and/ordownstream of the exemplified image processor. In the first embodiment,manual user input is required to provide colour emotion indiciaexpressed as emotional scale parameters, and image enhancement isapplied according to those indicia, on the basis of the equations above.The ability for a user to specify image enhancements in terms ofemotional (rather than technical) indicia is perceived to be asignificant improvement over prior art image enhancement techniques, andwould certainly find application in small scale photo printing orpersonal applications, where such user input would not be perceived asan undue overhead. In the second embodiment, a semi-automated example ofthe invention is provided, in which colour emotion indicia, expressed asemotional qualities for a given scene, are used to enhance images on thebasis of respective ‘ideal’ colour emotion. The third embodiment appliesautomated image enhancement on the basis of automatically-derived imageunderstanding techniques. This kind of example finds application, forexample, in large commercial-scale photo printing situations, or inembedded applications (for example in printers, cameras, or imageviewing applications such as web browsers), where user input orintervention might be perceived as impractical or an undue burden. Forexample, the third embodiment of the invention may find application inprocessing laboratories that may process many thousands or millions ofimages a day; where the overhead of manual data entry to characteriseimages understanding would be unacceptable.

The first exemplary embodiment of the present invention will now bedescribed with reference to FIG. 2. In FIG. 2, the image processorcomprises an image enhancement block 205, comprising an input 202 forreceiving data comprising an original image 210 and an input 204 forreceiving user-specified colour emotion indicia, which are used forvarying the emotional quality of the image. The image is originalinsofar as it is new to the image enhancement block 205: it may ofcourse have been processed in some way upstream of the image enhancementblock 205. The user-specified colour emotion indicia, in effect,constitute a colour emotion correction input to the image enhancementblock 205. The image enhancement block 205 also comprises an output 206for transmitting the enhanced output image data 230 downstream in thepipeline.

The image enhancement block 205 (and other blocks described hereinafter)comprises software code, written in an appropriate programming language,such a C++, which can execute on a programmable processor, for examplean Intel™ processor. If the block is implemented on a printer or camera,it may be embodied as compiled code, if there is an on-boardprogrammable processor, or as firmware controlling an embedded processoror even as a programmed ASIC.

According to the present embodiment, the colour emotion indicia arecolour activity, colour weight and colour warmth parameters, ascharacterised above. More particularly, in this embodiment, theparameters are specified using relative measures, for example, in termsof percentage increments or decrements relative to the original image.In practice, the values may be entered by a user via a graphical userinterface, for example comprising slider scales that can be manipulatedby the user; or in any other convenient manner. The system then uses theentered values to generate the appropriate k values within anappropriate encoding range (for example 0-255 in an 8-bit encodingscheme. In this embodiment, the system needs no indication of what theemotional quality of the original image is or what value the originalcolour emotion values are; it simply applies the equations according tothe input provided.

The second embodiment of the present invention is illustrated in FIG. 3.As in FIG. 2, the embodiment illustrated in FIG. 3 is an image processorthat comprises an image enhancement block 305, with an input 302 forreceiving image data, an input for receiving a colour emotion correctionand an output for transmitting the enhanced output image data 330downstream in the pipeline. In this embodiment, the colour emotioncorrection is generated by an image colour analyser in the form of animage colour analysis block 340, which receives a colour emotion indiciainput 320 from a user, in the form of an image scene classification, aidhas access to an image colour emotion database 350 of colour emotionsfor ideal scenes. The image colour analysis block 340 includes aselector 345, for selecting the appropriate colour emotions from thedatabase 350 on the basis of an image scene classification. The contentof the user input 320 can take various forms. In the present embodiment,the input classifies the kind of image in terms of the image scenario,for example ‘sports event’, ‘funeral’, ‘family scene’, to name just afew possible scenarios. Again, the choice of scene may be made by a userselecting one of various provided menu selections in a graphical userinterface. Equally, other techniques for classifying the image may beused. For example, known natural language understanding could be used toclassify an image as a ‘party’, on the basis of a user designation of“David's birthday photos”. Many alternative ways of classifying an imageon the basis of a user input may be applied without limitation.

The image colour analysis block 340, which also receives the image data310 as an input, computes the current emotion of the image scene andcompares that to colour emotions, stored in the database 350 andselected by the selector 345, for ‘ideal’ scenes of the designated kind.

According to the present embodiment, each ideal scene's colour emotionis a point in a colour emotion space defined by the three scales ofcolour activity, colour weight and colour warmth. For example, thecolour emotion of an image of a sports game might be characterised by ahigh value on the ‘active-passive’ axis, the colour emotion of an imageof a wedding might be characterised by high values on ‘warm-cool’ and‘heavy-light’ axes, and the colour emotion of an image of a funeral maybe characterised by low values on ‘warm-cool’ and ‘heavy-light’ axes.

The graph in FIG. 4 illustrates a colour emotion in the form of aprojection 400 of mean colour emotion coordinates. In this instance onlytwo dimensions of the colour emotion are shown, representing anactivity-warmth (x-y) plane. A first point 405 in the plane has arelatively low colour activity and a relatively high colour warmth, andis found to be typical for indoor portrait scenes. A second point 410has a relatively high colour activity and a relatively low colourwarmth, and is found to be typical for sunny landscape scenes. Variousother examples of two and three dimensional colour emotion spaces aredescribed in [1].

The image colour analysis block 340 determines the emotion of thecurrent image by expressing each pixel in the three-dimensionalcolour-emotion space (using the equations 6-8 from [1]) and calculatingthe mean of its pixels. Then, the selector 345 selects the appropriateideal colour emotion from the image colour emotion database 350, for thetype of image defined by the user, and compares the calculated value ofthe image colour emotion with the “ideal” colour emotion. If the valueis not the same as that required by the respective ideal colour emotion(or, indeed, within a certain threshold thereof), the colour analysisblock 340 supplies a respective colour emotion correction value, wherebythe image enhancement block 305 then shifts the image colours towardsthe ideal values, by applying the equations above. If the image colouremotion value is already close enough to the ideal colour emotionvalues, for example within a predetermined threshold, the imageenhancement block 305 is not required to enhance the image. One way ofdetermining whether the difference is greater than the threshold is toestimate the colour difference between the original image and acorrected image, and compare this with the Just Noticeable Differencethreshold, which is available from the colour science literature (forexample, according to Webber's Law, “Just Noticeable Difference” is theminimum amount by which stimulus intensity must be changed in order toproduce a noticeable variation in sensory experience). If the differenceis below the threshold then the correction will not have an appreciableeffect and is not applied. In embodiments of the invention, thisthreshold estimation is done using a thumbnail version of the image,having a greatly reduced number of pixels (for example 100 pixels on theshort dimension of the image rather than, perhaps, thousands of pixels),in order to speed the process up. Indeed, the image analysis block 340may in fact include a down-sampling process (not shown) and operateentirely on a down-sampled version of the original image data, in orderto increase efficiency.

In other embodiments, the user inputted colour emotion indicia mayinstead comprise a relative indication of the target colour emotion, forexample ‘make the scene happier’, ‘make the scene more exciting’ or thelike. Other ways of characterising the colour emotion indicia arepossible and should not be limited to the particular examples describedherein.

The third embodiment of the present invention is illustrated in thediagram in FIG. 4. According to this embodiment, the image processorcomprises an image enhancement block 505, which has an input 502 forreceiving original image data 510, an input 504 for receiving a colouremotion correction signal and an output 530 for providing enhanced imagedata downstream in a respective image processing pipeline. The colouremotion correction signal in this case is supplied by an imageunderstanding block 515, which also receives the original image data 510(which, again, can be down-sampled for performance improvement). Theimage understanding block 515 comprises an image content analyser in theform of an image analysis block 522, an image colour analysis block 540,including a selector 545, and an image colour emotion database 550,containing colour emotions of ideal scenes, generally as described forthe second embodiment. The image content analysis block 522 determinesan image classification (for example, whether the image is a weddingscene, a sports event scene or a landscape scene). Finally, there is anoptional global preference input 520, the purpose of which will bedescribed in more detail below, which can provide an input into eitheror both the image understanding block 515 or the image enhancement block505.

The image content analysis block 522 applies a range of known imageprocessing and analysis techniques to acquire image statistics and todiscern image elements such as objects, the presence of people, whetherit is an indoor or an outdoor image etc. From that data, the imageclassification (for example, an indoor domestic scene or an outdoorsports game scene) is determined automatically. Image understandingtechniques are well known and form a large and ever growing body ofliterature and research and development study. For example, known imagesegmentation techniques allow dividing an image area into perceptuallymeaningful regions and individual objects within the scene can berecognised [4], spatial configuration (perspective, occlusions, etc.)can be identified [5,6], and detection of human faces [7,8] is a rapidlydeveloping topic boosted by its numerous applications in imageenhancement, surveillance and others. Any one or more of these and,indeed, other known techniques can be applied in the image contentanalysis block 522 to classify an image scene automatically.

The output of the image content analysis block 522 is used, by theselector 545 to select an appropriate ideal scene colour emotion fromthe image colour emotion database 550. The image colour analysis block540 then generates a colour emotion correction signal, as described inthe second embodiment, and the image enhancement block 505 (if required)adjusts the image attributes to obtain the required emotional quality.

The optional global preference input 520 can be a manual or automatedinput process, which can provide information which is used by the imageunderstanding block 515 (or directly by the image enhancement block) tomodify the colour emotion correction signal for some or, indeed, allprocessed images within a group. For instance, the input might beresponsive to data indicating the kind of camera used to take therespective photos: for example, some cameras are known to reproducecolours in a way which renders images cool or inactive. Then, the inputmay be derived from a database of camera characteristics (not shown),which, for example, might dictate that images captured using the camerashould be enhanced to make them warmer or more active than mightotherwise be the case. In a manual input embodiment, the globalpreference input may permit a user to simply state that all imagesshould be warmer and lighter than the ideal for the kind of image beingprocessed (for example, simply because the photographer wants that to bethe case). In any event, the image understanding block (or imageenhancement block 505) uses the global preference input 520 as anadjustment to the colour emotion correction signal.

An exemplary automated image enhancement process will now be describedwith reference to the diagram in FIG. 4 and the flow diagram in FIG. 5.In a first step [600] original image data is input into the imageunderstanding block 515. The image content analysis block 522 analysesthe image [605] by applying known image understanding techniques inorder to classify the image [610]. The image classification is used[615] by the selector 545 to retrieve a respective ideal image colouremotion from the image colour emotion database 550. The image colouranalysis block 540 computes [620] an average image emotion valueC(image) [620] for the original image. The image colour analysis block540 determines [625] the difference (Delta) between the average imageemotion value of the original image C(image) and the average imageemotion value of an enhanced version of the image C(ideal), using athumbnail version of the image for reasons of efficiency. Next [630],the image colour analysis block 540 determines if the difference Deltais greater than a specified threshold. If not, the image data is passedthrough the pipeline and output [640] without any enhancement oradjustment of the image. If the difference is greater than the thresholdthen the image is enhanced [635] according to the selected ideal colouremotion, for example, using one or more of equations (1)-(3), and thenoutput [640].

According to embodiments of the invention, target pixels being correctedcould be all pixels in an image, if the entire image is being processed.This would most likely be the case for the first and second embodiments,in which relatively simple correction information can be provided by auser. However, in other embodiments, target pixels may be a subset ofall pixels in an image. For example, target pixels may be all pixels ina local area, for example defining a feature, region or person. Thisoption finds particular application in the third embodiment, in whichimage content is analysed and understood: it being possible then toidentify target pixels forming particular features, regions or people.In addition, or alternatively, target pixels may be a subset of allpixels defined in terms of class, for example pixels of a certain colour(or colour band). In any event, k values in respective correction modelsmay be constant for all pixels in an image (for example when all pixelsare target pixels), or may, for example, vary in a defined way (forexample, varying smoothly over an image region) when target pixels forman identified subset of an image, such as a feature, region or person.In general, there is no reason why the k values cannot be varied in anyappropriate way, determined by the skilled person, in order to imbue animage with the desired colour emotion character; and there is no reasonwhy k might not be different, in the limit, for every target pixel.

The above embodiments are to be understood as illustrative examples ofthe invention. Further embodiments of the invention are envisaged. Forexample, in each embodiment, the image processor is described in termsof particular blocks and elements. It will be appreciated that differentarrangements of blocks and elements may be used instead withoutdeparting from the invention. In addition, or alternatively, differentcolour emotion scales may be used and different colour emotion spacesmay be applied. It is to be understood that any feature described inrelation to any one embodiment may be used alone, or, if the contextpermits, in combination with other features described, and may also beused in combination with one or more features of any other of theembodiments, or any combination of any other of the embodiments.Furthermore, equivalents and modifications not described above may alsobe employed without departing from the scope of the invention, which isdefined in the accompanying claims.

The invention claimed is:
 1. Apparatus for processing an image,comprising: a memory storing processor-readable instructions; and aprocessor coupled to the memory, operable to execute the instructions,and based at least in part on the execution of the instructions operableto perform operations comprising receiving colour emotion indiciacorresponding to coordinates in a colour emotion space defined by colouremotion scales, based on the colour emotion indicia, identifying atleast one color emotion correction model that maps a coordinate of apixel value in a colour encoding space to an adjusted coordinate valuein the colour encoding space based on a respective value of a colourcorrection coefficient of the color emotion correction model,determining the respective value of the colour correction coefficientbased on the colour emotion indicia, mapping values of target pixels ofthe image in the colour encoding space to adjusted pixel values in thecolour encoding space, wherein the mapping comprises ascertaining arespective mapping function by applying the respective color correctioncoefficient values to the respective colour emotion correction model andapplying the mapping function to respective ones of the values of thetarget pixels, and outputting image pixel data based on the adjustedpixel values.
 2. Apparatus according to claim 1, wherein the colouremotion indicia are parameters of the colour emotion scales. 3.Apparatus according to claim 1, wherein the colour emotion indiciarepresent a point in the colour emotion space.
 4. Apparatus according toclaim 1, wherein the colour emotion space is defined by three colouremotion scales.
 5. Apparatus according to claim 2, wherein colouremotion scales are colour activity, colour weight and colour warmth. 6.Apparatus according to claim 5, wherein the mapping comprises, for eachtarget pixel having respective CIELAB colour coordinate values L*, a*,and b*, determining colour activity adjusted CIELAB color coordinatevalues L*_(a), a*_(a), and b*_(a) for the target pixel in accordancewith: L_(a)^(*) = L₀^(*)(1 + k_(a)) + L_(r)a_(a)^(*) = a₀^(*)(1 + k_(a)) + a_(r)$b_{a}^{*} = {{b_{0}^{*}\left( {1 + \frac{k_{a}}{1.4}} \right)} + b_{r}}$wherein k_(a) is a correction coefficient for the target pixel, and L*₀,a*₀, and b*₀ are the CIELAB colour coordinates of the target pixelshifted to a color emotion reference having CIELAB colour coordinatesL_(r), a_(r), and b_(r).
 7. Apparatus according to claim 5, wherein themapping comprises, for each target pixel having respective CIELAB colourcoordinate values L, a, and b, a CIELAB hue angle value h, and a chromavalue C, determining colour weight adjusted CIELAB color coordinatevalues L_(w) and b_(w) for the target pixel in accordance with:$h_{w} = \left\{ {{\begin{matrix}{{h_{0} + {h_{0}\frac{\left( {180 - h_{0}} \right)}{180}w_{C}k_{w}} + h_{r}};} & {h_{0} \geq 180} \\{{h_{0} + {\left( {360 - h_{0}} \right)\frac{\left( {h_{0} - 180} \right)}{180}w_{C}k_{w}} + h_{r}};} & {h_{0} > 180}\end{matrix}L_{w}} = {100\left( \frac{L}{100} \right)^{\gamma}}} \right.$wherein k_(w) is a correction coefficient for the target pixel, h₀ isthe CIELAB colour coordinates of the target pixel shifted to a weightcolor emotion reference hue h_(r), and w_(c) is a weighting factor thatis set according to a chroma weighting function of the chroma value C.8. Apparatus according to claim 5, wherein the mapping comprises, foreach target pixel having respective CIELAB colour coordinate values L,a, and b, a CIELAB hue angle value h, and a chroma value C, determiningcolour warmth adjusted CIELAB color coordinate hue values h_(w) andC_(w) for the target pixel in accordance with$h_{w} = \left\{ \begin{matrix}{{h_{0} + {h_{0}\frac{\left( {180 - h_{0}} \right)}{180}w_{C}k_{w}} + h_{r}};} & {h_{0} \geq 180} \\{{h_{0} + {\left( {360 - h_{0}} \right)\frac{\left( {h_{0} - 180} \right)}{180}w_{C}k_{w}} + h_{r}};} & {h_{0} > 180}\end{matrix} \right.$ and determining colour warmth adjusted CIELABcolor coordinate chroma values C_(w) for the target pixel in accordancewith a chroma weighting function of the chroma value C, wherein k_(w) isa correction coefficient for the target pixel, h₀ is the CIELAB colourcoordinates of the target pixel shifted to a warmth color emotionreference hue h_(r), and w_(c) is a weighting factor that is setaccording to chroma weighing function of the chroma value C. 9.Apparatus according to claim 6, wherein each adjustment is determinedrelative to a specified colour emotion reference.
 10. An imageprocessor, comprising: an input to receive image pixel data, an input toreceive colour emotion indicia; an input to receive a sceneclassification; and a selector to select on the basis of theclassification one from a plurality of stored colour emotions for idealimage scenes, wherein the processor is arranged to adjust target pixelsof the image in a colour encoding space on the basis of the colouremotion indicia and to output image pixel data resulting therefrom. 11.An image processor according to claim 10, comprising a colour analyserto calculate and compare an emotion of image pixel data of an inputtedimage, having an associated classification, with a selected emotion fora respective ideal image scene for the classification.
 12. An imageprocessor according to claim 11, wherein the colour emotion of imagepixel data of an inputted image is a statistical function of the colouremotion of the target pixels.
 13. An image processor according to claim12, wherein the colour emotion of image pixel data of an inputted imageis calculated to be the mean colour emotion of the target pixels.
 14. Animage processor according to claim 11, comprising a content analyser toanalyse image pixel data of an inputted image and generate, on the basisof identified content, a respective image classification for input tothe colour analyser.
 15. A method for processing an image, comprising:receiving colour emotion indicia corresponding to coordinates in acolour emotion space defined by colour emotion scales' based on thecolour emotion indicia, identifying at least one color emotioncorrection model that maps a coordinate of a pixel value in a colourencoding space to an adjusted coordinate value in the colour encodingspace based on a respective value of a colour correction coefficient ofthe color emotion correction model, determining the respective value ofthe colour correction coefficient based on the colour emotion indicia,mapping values of target pixels of the image in the colour encodingspace to adjusted pixel values in the colour encoding space, wherein themapping comprises ascertaining a respective mapping function by applyingthe respective color correction coefficient values to the respectivecolour emotion correction model and applying the mapping function torespective ones of the values of the target pixels, and outputting imagepixel data based on the adjusted pixel values.
 16. Apparatus accordingto claim 1, further comprising determining a colour emotion referencecoordinate in the colour encoding space based on values of pixels of theimage, and wherein the at least one color emotion correction model mapsa coordinate of a pixel value in the colour encoding space to anadjusted coordinate value in the colour encoding space relative to thecolour emotion reference coordinate.